BuildKit’s --output flag is where this gets practical. You can tell BuildKit to export the result as:
使用过程中，用户仅需描述目标，系统就会自动生成子智能体，分别执行网络搜索、文档生成、数据处理或 API 调用等任务。
这个过程中产生的价值，体现在推理轨迹，而推理轨迹是很难通过蒸馏习得的——至少现在是这样。，详情可参考heLLoword翻译官方下载
As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
。51吃瓜对此有专业解读
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